

"""
Reference:
AidenDurrant: https://raw.githubusercontent.com/AidenDurrant/MoCo-Pytorch/master/src/model/network.py
"""


import torch
import torch.nn as nn
from .base import AdversarialDefensiveModule
from .layerops import Sequential


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnext50_32x4d']

def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(AdversarialDefensiveModule):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(AdversarialDefensiveModule):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(AdversarialDefensiveModule):
    def __init__(self, block, layers, num_classes=1000, strides=(1,2,2,2),
                zero_init_residual=False, groups=1, width_per_group=64, 
                replace_stride_with_dilation=None, norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        if isinstance(strides, str):
            strides = list(map(int, strides))

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
                                bias=False)  # For CIFAR

        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1],
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2],
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3],
                                       dilate=replace_stride_with_dilation[2])

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.fc = nn.Linear(512 * block.expansion, num_classes)


        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x) # 64 x 32 x 32
        x = self.bn1(x) 
        x = self.relu(x)

        x = self.layer1(x) # 64 x 32 x 32
        x = self.layer2(x) # 128 x 16 x 16
        x = self.layer3(x) # 256 x 8 x 8
        x = self.layer4(x) # 512 x 4 x 4

        # if not self.attacking:
        #     return x
        x = self.avgpool(x) # 512 x 1 x 1
        features = torch.flatten(x, 1)

        outs = self.fc(features)

        return outs


def resnet18(num_classes, **kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    Args:
        args: arguments
    """
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, **kwargs)


def resnet34(num_classes, **kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    Args:
        args: arguments
    """
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, **kwargs)


def resnet50(num_classes, **kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    Args:
        args: arguments
    """
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, **kwargs)


def resnet101(num_classes, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    Args:
        args: arguments
    """
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, **kwargs)


# def resnet152(num_classes, **kwargs):
#     r"""ResNet-152 model from
#     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
#     Args:
#         args: arguments
#     """
#     return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, **kwargs)


def resnext50_32x4d(num_classes, **kwargs):
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, **kwargs)


# def resnext101_32x8d(num_classes, **kwargs):
#     r"""ResNeXt-101 32x8d model from
#     `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
#     Args:
#         pretrained (bool): If True, returns a model pre-trained on ImageNet
#         progress (bool): If True, displays a progress bar of the download to stderr
#     """
#     kwargs['groups'] = 32
#     kwargs['width_per_group'] = 8
#     return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, **kwargs)


# def wide_resnet50_2(num_classes, **kwargs):
#     r"""Wide ResNet-50-2 model from
#     `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
#     The model is the same as ResNet except for the bottleneck number of channels
#     which is twice larger in every block. The number of channels in outer 1x1
#     convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
#     channels, and in Wide ResNet-50-2 has 2048-1024-2048.
#     Args:
#         pretrained (bool): If True, returns a model pre-trained on ImageNet
#         progress (bool): If True, displays a progress bar of the download to stderr
#     """
#     kwargs['width_per_group'] = 64 * 2
#     return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, **kwargs)


# def wide_resnet101_2(num_classes, **kwargs):
#     r"""Wide ResNet-101-2 model from
#     `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
#     The model is the same as ResNet except for the bottleneck number of channels
#     which is twice larger in every block. The number of channels in outer 1x1
#     convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
#     channels, and in Wide ResNet-50-2 has 2048-1024-2048.
#     Args:
#         pretrained (bool): If True, returns a model pre-trained on ImageNet
#         progress (bool): If True, displays a progress bar of the download to stderr
#     """
#     kwargs['width_per_group'] = 64 * 2
#     return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, **kwargs)




if __name__ == "__main__":
    
    model = resnet18(10)
    x = torch.randn(10, 3, 32, 32)
    model(x)